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UCx: Advanced Bayesian Statistics Using R

Now that you know the basics of Bayesian inference, dive deeper to explore its richness and flexibility more fully. Let’s take a closer look at modeling latent variables, Bayesian model averaging, generalised linear models, and MCMC methods

6 semanas
5–10 horas por semana
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Comienza el 28 mar
Termina el 28 ago

Sobre este curso

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Advanced Bayesian Data Analysis Using R is part two of the Bayesian Data Analysis in R professional certificate.

This course is directed at people who are already familiar with the fundamentals of Bayesian inference. It explores further the concepts, methods, and algorithms introduced in the part one (Introductory Bayesian Data Analysis Using R).

The course places mixed effects regression models useful for experiments with repeated measures or additional hierarchy often encountered in biostatistics, ecology and health sciences among others within the Bayesian context. It takes a closer look at the Markov Chain Monte Carlo (MCMC) algorithms, why they work and how to implement them in the R programming language. Convergence assessment and visualisation of the results are discussed in some detail. The course also explores Bayesian model averaging, often used in machine learning, all within the context of practical examples.

Finally, we discuss different kinds of missing data, and the Bayesian methods of dealing with such situations.

Prior facility in basic algebra and calculus as well as programming in R is highly recommended.

De un vistazo

  • Institución: UCx
  • Tema: Análisis de datos
  • Nivel: Advanced
  • Prerrequisitos:

    Prior facility in basic algebra and calculus as well as programming in R is highly recommended.

  • Programas asociados:
  • Idioma: English
  • Transcripción de video: English
  • Habilidades asociadas:Missing Data, Markov Chain Monte Carlo, R (Programming Language), Health Sciences, Calculus, Elementary Algebra, Algorithms, Machine Learning, Linear Model, Bayesian Statistics, Ecology, Bayesian Inference, Biostatistics, Data Analysis, Bayesian Modeling, Repeated Measures Design

Lo que aprenderás

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Using latent (unobserved) variables and dealing with missing data.

Multivariate analysis within the context of mixed effects linear regression models. Structure, assumptions, diagnostics and interpretation. Posterior inference and model selection.

Why Monte Carlo integration works and how to implement your own MCMC Metropolis-Hastings algorithm in R.

Bayesian model averaging in the context of change-point problem. Pinpointing the time of change and obtaining uncertainty

¿Quién puede hacer este curso?

Lamentablemente, las personas residentes en uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.

Este curso es parte del programa Bayesian Statistics Using R Professional Certificate

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Instrucción por expertos
2 cursos de capacitación
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3 meses
5 - 10 horas semanales

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